Extract text, tables, images, metadata, and code intelligence from 107 file formats and 371 programming languages including PDF, Office documents, images, and audio/video transcripts where native transcription is available. Swift bindings via swift-bridge for macOS and iOS, with native Swift types and async/await support.

• Document intelligence core — extract text, tables, images, metadata, entities, keywords, code intelligence, and transcripts in builds that enable transcription.
• Format coverage — PDF, Office, images, HTML/XML, email, archives, notebooks, citations, scientific formats, plain text, and audio/video formats in builds that enable transcription.
• OCR choices — Tesseract, PaddleOCR, Candle where supported, VLM OCR through liter-llm, and plugin hooks for custom backends.
• Same engine as every binding — Rust, Python, Node.js, Go, Java, PHP, Ruby, .NET, Elixir, WASM, Kotlin Android, Swift, Dart, Zig, and C FFI share the same Rust implementation.
• SwiftPM package — Swift Concurrency API for Apple targets.

Add to your Package.swift dependencies:

.package(url: "https://github.com/xberg-io/xberg.git", from: "1.2.7"),

Then add the product to the relevant target:

.target(
    name: "YourTarget",
    dependencies: [
        .product(name: "Xberg", package: "xberg"),
    ]
),

• Swift 6.0+ (swift-tools-version: 6.0) on macOS 13+ or iOS 16+
• Native runtime delivered through the C FFI surface from xberg-ffi; published artifacts ship as a binary target
• Optional: ONNX Runtime version 1.24+ for ORT-dependent inference features
• Optional: Tesseract OCR for OCR functionality

Extract text, metadata, and structure from any supported document format:

import Xberg

let input = #"{"kind":"uri","uri":"document.pdf"}"#
let output = try await extract(input, "{}")

print("Results: \(output.summary().results())")

Most use cases benefit from configuration to control extraction behavior:

With OCR (for scanned documents):

import Foundation
import Xberg
import RustBridge

let configJson = """
{
    "ocr": {
        "backend": "tesseract",
        "language": "eng"
    }
}
"""

let config = try extractionConfigFromJson(configJson)
let input = try extractInputFromJson(#"{"kind":"uri","uri":"scanned.pdf"}"#)
let resultOutput = try await extract(input: input, config: config)
let result = resultOutput.results().get(index: 0)!

print(result.content().toString())

See Configuration Guide for table extraction options.

import Xberg

let inputs = try [
    Xberg.extractInputFromJson(#"{"kind":"uri","uri":"report.pdf"}"#),
    Xberg.extractInputFromJson(#"{"kind":"uri","uri":"notes.txt"}"#),
]
let config = try Xberg.extractionConfigFromJson("{}")
let output = try await Xberg.extractBatch(inputs: inputs, config: config)

for result in output.results() {
    print(result.content().toString())
}

For non-blocking document processing:

import Xberg

let input = #"{"kind":"uri","uri":"document.pdf"}"#
let output = try await extract(input, "{}")

print("Results: \(output.summary().results())")

• Installation Guide - Platform-specific setup
• API Documentation - Complete API reference
• Examples & Guides - Full code examples and usage guides
• Configuration Guide - Advanced configuration options

107 formats across 140 unique file extensions, with 56 compatibility MIME aliases, intelligent format detection, and comprehensive metadata extraction.

Powered by tree-sitter-language-pack — documentation.

Complete Format Reference

• Text Extraction - Extract all text content with position and formatting information
• Metadata Extraction - Retrieve document properties, creation date, author, etc.
• Table Extraction - Parse tables with structure and cell content preservation
• Image Extraction - Extract embedded images and render page previews
• Audio/Video Transcription - Extract speech transcripts from MP3, M4A, WAV, WebM, and MP4 inputs when the native transcription feature is available
• OCR Support - Integrate multiple OCR backends for scanned documents
• Async/Await - Non-blocking document processing with concurrent operations
• Plugin System - Extensible post-processing for custom text transformation
• Embeddings - Generate vector embeddings using ONNX Runtime models or provider-hosted services
• Batch Processing - Efficiently process multiple documents in parallel
• Memory Efficient - Stream large files without loading entirely into memory
• Language Detection - Detect and support multiple languages in documents
• Code Intelligence - Extract structure, imports, exports, symbols, and docstrings from 371 programming languages via tree-sitter
• Configuration - Fine-grained control over extraction behavior
• Six Output Formats - Plain text, Markdown, Djot, HTML, JSON tree structure, or Docling DocTags

Xberg supports multiple OCR backends for extracting text from scanned documents and images:

• Tesseract

• Paddleocr

• Sceptre

import Foundation
import Xberg
import RustBridge

let configJson = """
{
    "ocr": {
        "backend": "tesseract",
        "language": "eng"
    }
}
"""

let config = try extractionConfigFromJson(configJson)
let input = try extractInputFromJson(#"{"kind":"uri","uri":"scanned.pdf"}"#)
let resultOutput = try await extract(input: input, config: config)
let result = resultOutput.results().get(index: 0)!

print(result.content().toString())

This binding provides full async/await support for non-blocking document processing:

import Xberg

let input = #"{"kind":"uri","uri":"document.pdf"}"#
let output = try await extract(input, "{}")

print("Results: \(output.summary().results())")

Xberg supports extensible post-processing plugins for custom text transformation and filtering.

For detailed plugin documentation, visit Plugin System Guide.

Generate vector embeddings for extracted text using the built-in ONNX Runtime support. Requires ONNX Runtime installation.

Embeddings Guide

Process multiple documents efficiently:

import Xberg

let inputs = try [
    Xberg.extractInputFromJson(#"{"kind":"uri","uri":"report.pdf"}"#),
    Xberg.extractInputFromJson(#"{"kind":"uri","uri":"notes.txt"}"#),
]
let config = try Xberg.extractionConfigFromJson("{}")
let output = try await Xberg.extractBatch(inputs: inputs, config: config)

for result in output.results() {
    print(result.content().toString())
}

For advanced configuration options including language detection, table extraction, OCR settings, and more:

Configuration Guide

• Official Documentation
• API Reference
• Examples & Guides

Contributions are welcome! See Contributing Guide.

• Xberg — the open-source content-intelligence engine: text, tables, and metadata from 107 formats (141 file extensions), with OCR, transcription, and code intelligence. MIT.
• Xberg Pro — a complete self-hosted content-intelligence backend in a single container. Commercial.
• Xberg Enterprise — the distributed, governed content-intelligence platform, scaled on Kubernetes with team governance and support. Commercial.
• crawlberg — web crawling and scraping with HTML→Markdown and headless-Chrome fallback.
• html-to-markdown — fast, lossless HTML→Markdown engine.
• liter-llm — universal LLM API client with native bindings for 14 languages and 165 providers.
• tree-sitter-language-pack — tree-sitter grammars and code-intelligence primitives.
• alef — the polyglot binding generator that produces this README and all per-language bindings.
• Discord — community, roadmap, announcements.

MIT License — see LICENSE for details.

• Discord Community: Join our Discord
• GitHub Issues: Report bugs
• Discussions: Ask questions